The database systems course in an undergraduate computer science degree program is gaining increasing importance due to the con- tinuous supply of database-related jobs as well as the rise of Data Science. A key learning goal of learners taking such a course is to understand how sql queries are executed in an rdbms in practice. Existing rdbms typically expose a query execution plan (qep) in visual or textual format, which describes the execution steps for a given query. However, it is often daunting for a learner to compre- hend these qeps containing vendor-specific implementation details. In this demonstration, we present a novel, generic, and portable system called lantern that generates a natural language-based description of the execution strategy chosen by the underlying rdbms to process a query. It provides a declarative framework called pool for subject matter experts (sme) to efficiently create and ma- nipulate natural language descriptions of physical operators of any rdbms. It then exploits pool to generate nl description of a qep by integrating rule-based and deep learning-based techniques to infuse language variability in the descriptions. Such nl generation strategy mitigates the impact of boredom on learners caused by repeated exposure of similar text generated by rule-based systems.